这篇论文发现聊天机器人自己认错比让别人纠正更能维护信任，而且和用户关系越好效果越好，实测120人，结果很实用。
一项针对120人的实验比较了社交聊天机器人三种纠错策略：网页撤回、自我纠正和专家聊天机器人纠正。结果显示三种策略均能纠正错误，但只有自我纠正不损害聊天机器人的可信度（信任度和专业感知评分更高）。用户与聊天机器人的社交连接强度（社交吸引力、自我披露）显著预测信念改变幅度，但仅在自我纠正时成立。外部来源纠正会切断社交连接与信念改变之间的关联。
Correct Yourself, Keep My Trust: How Self-Correction and Social Connection Shape Credibility in Social Chatbots
When social chatbots make mistakes, and they do, how they recover determines whether users trust them again. Social chatbots are increasingly integrated into everyday life, yet they remain prone to generating convincing but inaccurate information. The social connection they build with users makes such errors particularly consequential. We conducted a between-subjects experiment (N=120) comparing three error correction strategies: a webpage retraction, self-correction by the same social chatbot, and correction by an expert chatbot. Our results reveal two key findings. First, all three strategies corrected the error equally well, but only self-correction did so without damaging the chatbot's credibility: participants rated self-correcting chatbots significantly higher in both trustworthiness and perceived expertise than chatbots whose errors were corrected by external sources. Second, the strength of the user's social connection with the chatbot, measured through social attraction and self-disclosure, significantly predicted the magnitude of belief change, but only when the chatbot corrected itself. Outsourcing corrections to an external source severed this link entirely. These findings suggest that social chatbots should correct their own mistakes rather than outsource corrections, and that investing in social connection is a functional mechanism that amplifies correction effectiveness, not merely a design feature. We discuss implications for designing chatbots that maintain long-term credibility while effectively addressing their own errors.